Urban Mobility: Leveraging AI, Machine Learning, and Data Analytics for Smart Transportation Planning- A case study on New York City
Ashish Makanadar, Samit Shahane
Abstract
This research paper explores the utilization of AI, machine learning, and data analytics for smart transportation planning to achieve sustainable urban mobility. The study aims to address two key research questions: (1) How can these advanced technologies be leveraged to optimize urban transportation systems? (2) What are the potential benefits associated with their implementation? The research methodology involves collecting relevant transportation data and applying statistical analysis techniques to uncover patterns and correlations. Additionally, a simulation model is developed and calibrated using real-world data to evaluate different transportation scenarios. Performance evaluation metrics, such as travel time, congestion levels, and environmental impact, are employed to assess the effectiveness of the strategies. The findings of this research demonstrate improved transportation efficiency, reduced congestion, and enhanced sustainability in New York City urban areas. However, limitations include data availability, assumptions made during modeling, and the context-specific nature of the results. These findings contribute to evidence-based decision-making in civil engineering, offering valuable insights for stakeholders and urban planners in their efforts toward sustainable urban mobility.